{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最佳预测点数量相关特征分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/wangnaixuan/Documents/GitHub/prediction_service/notebooks', '/usr/local/anaconda3/envs/nash/lib/python37.zip', '/usr/local/anaconda3/envs/nash/lib/python3.7', '/usr/local/anaconda3/envs/nash/lib/python3.7/lib-dynload', '', '/usr/local/anaconda3/envs/nash/lib/python3.7/site-packages', '/usr/local/anaconda3/envs/nash/lib/python3.7/site-packages/IPython/extensions', '/Users/wangnaixuan/.ipython', '/Users/wangnaixuan/Documents/GitHub/prediction_service']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\")))\n",
    "print(sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/wangnaixuan/Documents/GitHub/prediction_service/data/bpic2012_filter_w_pred_v2_1.csv\n"
     ]
    }
   ],
   "source": [
    "from config import Variables\n",
    "\n",
    "data_path = Variables.get_data_path()\n",
    "print(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 统计每个样本的最佳预测情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "186103 [0, 1, 2, 4, 23, 24, 25, 26]\n",
      "198391 [0, 2, 3, 10, 11, 15, 19, 26, 28, 33]\n",
      "202494 [0, 4, 11, 12, 15, 16]\n",
      "179968 [0, 2, 4, 8, 10, 11, 13, 15, 16, 18, 20]\n",
      "194305 [0, 2, 3, 6, 9, 10, 20]\n",
      "190210 [0, 1, 2, 5, 7, 9, 23, 24, 29, 30]\n",
      "206594 [0, 1, 4, 5, 6, 8, 9, 14, 19, 22, 24, 25]\n",
      "182020 [0, 2, 5, 20, 22, 23, 25, 27, 32, 39, 41, 48, 69, 70, 71]\n",
      "190213 [0, 2, 4, 42, 43, 45, 47, 50, 53]\n",
      "206597 [0, 1, 4, 5, 6, 7, 13, 17, 22, 24, 26, 27]\n",
      "179974 [0, 1, 2, 10, 12, 14, 16]\n",
      "210704 [0, 5, 6, 7, 9, 60, 61, 62, 70, 71]\n",
      "208658 [0, 13, 14, 15, 17, 19]\n",
      "177941 [0, 22, 68, 69, 74, 80, 81]\n",
      "206615 [0, 15, 25, 26, 27, 28, 29, 30, 32, 34, 35, 38, 42, 44, 50, 61, 64, 68, 69, 70, 71, 79, 80]\n",
      "206621 [0, 1, 4, 5, 7, 11, 19]\n",
      "210719 [0, 5, 7, 28, 38]\n",
      "190240 [0, 2, 4, 18, 28, 34, 35, 36, 37]\n",
      "206624 [0, 1, 4, 34, 35]\n",
      "200482 [0, 4, 5, 10, 17, 18]\n",
      "184099 [0, 1, 27, 35, 36, 37, 38]\n",
      "210725 [0, 1, 12, 14, 17, 18, 31, 32]\n",
      "186151 [0, 1, 7, 13, 14, 19, 20, 23, 41, 42, 43, 44]\n",
      "204586 [0, 1, 3, 4, 6, 8, 19, 54]\n",
      "173868 [0, 2, 3, 7, 9, 11, 24, 25]\n",
      "173871 [0, 1, 4, 5, 6, 24, 27, 28, 32, 33]\n",
      "177968 [0, 20, 33, 34, 39, 40, 46, 47, 48, 51]\n",
      "184114 [0, 1, 5, 7, 8, 9, 10, 14, 15, 16, 17, 25, 33, 34]\n",
      "190258 [0, 2, 15, 16, 17, 19, 23, 28]\n",
      "190264 [0, 1, 2, 22]\n",
      "194362 [0, 21, 25, 27]\n",
      "200506 [0, 1, 33]\n",
      "200509 [0, 1, 2, 24]\n",
      "204607 [0, 5, 6, 7, 8, 15, 24, 25]\n",
      "198469 [0, 1, 22]\n",
      "198475 [0, 1, 23, 35, 36]\n",
      "212812 [0, 4, 5, 11, 26]\n",
      "200524 [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
      "192334 [0, 2, 5, 6, 7, 9, 24, 25, 26, 38]\n",
      "206669 [0, 2, 3, 22, 24]\n",
      "177998 [0, 5, 6, 7, 8, 9, 11, 14, 15, 30, 50]\n",
      "198481 [0, 2, 3, 4, 9, 10, 11, 15, 18, 23, 33]\n",
      "204628 [0, 1, 2, 4, 6, 8, 10, 11, 37, 38, 43]\n",
      "180052 [0, 2, 4, 6, 7, 13, 14, 15, 16, 21]\n",
      "178004 [0, 2, 3, 32]\n",
      "212824 [0, 4, 10, 15]\n",
      "206685 [0, 1, 2, 5, 6, 7, 8, 9, 11, 13, 14, 22, 23, 30]\n",
      "180064 [0, 1, 12, 13, 16, 17]\n",
      "198496 [0, 4, 5, 9, 44]\n",
      "212833 [0, 4, 5]\n",
      "196452 [0, 1, 2, 3, 32, 33, 34]\n",
      "194406 [0, 6, 7, 9, 10, 11, 13, 14, 15, 25, 28]\n",
      "210791 [0, 1, 4, 5, 6, 13, 21, 22, 24, 25]\n",
      "180070 [0, 21, 26, 27, 29, 32, 45, 65]\n",
      "210794 [0, 1, 2, 11, 27, 28]\n",
      "204655 [0, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16]\n",
      "182128 [0, 34]\n",
      "206706 [0, 1, 2, 7, 8, 9, 11, 17, 18, 19, 30]\n",
      "184180 [0, 1, 53, 56, 57, 58]\n",
      "182137 [0, 1, 3, 6, 7, 10, 32]\n",
      "192383 [0, 1, 14, 15, 16, 19]\n",
      "200578 [0, 1, 3, 5, 7, 9, 30, 31, 34]\n",
      "204676 [0, 2, 3, 8, 9, 10, 12, 19, 20, 28, 39]\n",
      "182149 [0, 1, 2, 3, 14, 15, 16, 17, 18, 21, 39, 40, 41]\n",
      "180103 [0, 3, 13, 14, 15, 16, 17, 18, 23, 24, 28, 31]\n",
      "204685 [0, 5, 12, 14, 21]\n",
      "212878 [0, 1, 7]\n",
      "198543 [0, 1, 2, 3, 5, 60, 61, 62, 73, 74]\n",
      "202641 [0, 1, 3, 4, 8, 16, 17, 21, 22, 23]\n",
      "202647 [0, 1, 2, 3, 8, 11, 21, 31, 33, 34, 39, 40, 41, 43, 47, 49, 51, 53, 54]\n",
      "184216 [0, 1, 4, 15, 23]\n",
      "208793 [0, 6, 9, 13, 14, 24, 26, 27, 31, 43]\n",
      "212887 [0, 1, 4, 7]\n",
      "173979 [0, 4, 8, 14, 16, 18, 19]\n",
      "192413 [0, 4, 15, 21, 34, 36, 41, 44]\n",
      "206751 [0, 1, 2, 9, 22]\n",
      "190369 [0, 1, 4, 5, 6, 7, 13]\n",
      "204709 [0, 1, 3, 5, 13, 31, 53]\n",
      "208805 [0, 1, 2, 4, 5, 21, 23, 24]\n",
      "184231 [0, 1, 7, 18]\n",
      "200617 [0, 1, 2, 14, 15, 16]\n",
      "180139 [0, 1, 4, 5, 7, 15]\n",
      "196524 [0, 2, 6, 8, 12, 13, 14, 48, 49, 51, 56, 57]\n",
      "176045 [0, 1, 3, 7, 8, 17]\n",
      "180145 [0, 2, 30]\n",
      "210866 [0, 19, 20, 23, 24, 32, 36]\n",
      "212914 [0, 18, 23]\n",
      "174009 [0, 1, 2, 3, 4, 10, 13, 22, 27, 29]\n",
      "212923 [0, 1, 24]\n",
      "176060 [0, 21]\n",
      "174012 [0, 22, 38]\n",
      "202686 [0, 2, 3, 4, 16, 17, 19, 20, 21, 23]\n",
      "208832 [0, 1, 2, 21, 23, 25, 42, 45, 47, 49, 51, 52]\n",
      "182209 [0, 4, 13, 15, 16, 17, 37, 38, 39]\n",
      "204736 [0, 1, 2, 16]\n",
      "200644 [0, 27, 28, 32]\n",
      "192455 [0, 2, 4, 9, 10, 12, 13, 15, 18, 19]\n",
      "184267 [0, 1, 3, 4, 11, 42, 43]\n",
      "194508 [0, 1, 2, 3, 4, 42, 43]\n",
      "188365 [0, 2, 4, 6, 12, 13, 14, 25, 29, 40]\n",
      "204751 [0, 36, 38, 39]\n",
      "200656 [0, 2, 3, 4, 11, 13, 14, 16, 20, 21]\n",
      "184273 [0, 1, 4, 5, 13, 42, 43, 45]\n",
      "194514 [0, 6, 8, 9, 10, 11, 13, 14, 30]\n",
      "192467 [0, 1, 5, 18, 19, 22, 23, 30]\n",
      "180181 [0, 1, 4, 5, 6, 7, 14, 28, 30, 31]\n",
      "198618 [0, 1, 3, 4, 9, 10, 12, 14, 24, 25, 26, 28, 29, 36, 67, 72, 73]\n",
      "180187 [0, 2, 5, 7, 9, 11, 21, 24, 25, 27, 28, 30, 31]\n",
      "196578 [0, 2, 5, 22, 23, 24, 28, 39, 40, 46, 50, 82]\n",
      "198627 [0, 1, 3, 26, 27, 30, 31, 32, 33, 49, 50, 51, 52, 57, 62, 64, 65, 68, 69, 70, 71, 88, 89]\n",
      "194532 [0, 16, 18, 20, 21, 26, 33, 45]\n",
      "210917 [0, 1, 2, 3, 7, 12, 14]\n",
      "182245 [0, 3, 4, 61, 62]\n",
      "194535 [0, 16, 20, 22, 27]\n",
      "212965 [0, 1, 3, 4, 5]\n",
      "180202 [0, 3, 4, 9, 18, 19, 22, 23]\n",
      "210926 [0, 1, 2, 3, 11, 14, 16, 20, 21]\n",
      "212977 [0, 1, 6]\n",
      "198642 [0, 1, 4, 81]\n",
      "174078 [0, 1, 2, 3, 5, 25]\n",
      "1000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from typing import Dict, List\n",
    "from baselines.prediction_policy import Policy\n",
    "from environment.trace_sample import Trace\n",
    "\n",
    "data_frame = pd.read_csv(data_path)\n",
    "\n",
    "def best_predictions() -> Dict[float, List]:\n",
    "    best_preds = {}\n",
    "    for cid in list(set(data_frame['case:concept:name'].values)):\n",
    "        print(cid, end=\" \")\n",
    "        pred_indexes = Policy.best_prediction_indexes(Trace(data_path, cid))\n",
    "        print(pred_indexes)\n",
    "        best_preds[cid] = pred_indexes\n",
    "    return best_preds\n",
    "        \n",
    "best_preds = best_predictions()\n",
    "print(len(best_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "### 构建x和y\n",
    "first_p = []  # 第一个点\n",
    "first_10 = []  # 前5点平均值\n",
    "last_10 = []  # 后10点平均值\n",
    "true_values = []  # 真实值\n",
    "\n",
    "for cid in list(set(data_frame['case:concept:name'].values)):\n",
    "    avg_p1 = np.mean(data_frame[data_frame['case:concept:name']==cid]['total_time_pred'][:10].values)\n",
    "    avg_p2 = np.mean(data_frame[data_frame['case:concept:name']==cid]['total_time_pred'][-10:].values)\n",
    "    first_p.append(data_frame[data_frame['case:concept:name']==cid]['total_time_pred'].iloc[0])\n",
    "    first_10.append(avg_p1)\n",
    "    last_10.append(avg_p2)\n",
    "    true_values.append(data_frame[data_frame['case:concept:name']==cid].iloc[0]['total_time_true'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Case 1:\n",
    "- 前面预测的不准，后面预测的准"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 前10点与真实值的相关性\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(first_10, true_values, '.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 后10点与真实值的相关性\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(last_10, true_values, '.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Case 2:\n",
    "- 前面信息较少，预测值比较集中，只依据贷款金额、trace开始时间等特征辅助预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第一个点的预测值\n",
    "import pandas as pd\n",
    "\n",
    "df_fp = pd.DataFrame({\"value\": first_p})\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "ax1 = fig.add_subplot(211)\n",
    "df_fp.hist(bins=30, alpha=0.5, ax=ax1)\n",
    "df_fp.plot(kind='kde', secondary_y=True, ax=ax1)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 真实值分布\n",
    "df_tv = pd.DataFrame({\"value\": true_values})\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "ax2 = fig.add_subplot(211)\n",
    "df_tv.hist(bins=30, alpha=0.5, ax=ax2)\n",
    "df_tv.plot(kind='kde', secondary_y=True, ax=ax2)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Case 3:\n",
    "- 存在许多event发生的时间非常接近，预测结果应该会比较接近\n",
    "- 特定的活动（可能存在）会停留较长的时间，此时可能会引起预测结果的较大变动，可视为milestone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>org:resource</th>\n",
       "      <th>concept:name</th>\n",
       "      <th>time:timestamp_short</th>\n",
       "      <th>case:REG_DATE_short</th>\n",
       "      <th>case:concept:name</th>\n",
       "      <th>case:AMOUNT_REQ</th>\n",
       "      <th>time_spent</th>\n",
       "      <th>total_time_pred</th>\n",
       "      <th>total_time_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33570</th>\n",
       "      <td>112.0</td>\n",
       "      <td>A_SUBMITTED_COMPLETE</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.969542</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33571</th>\n",
       "      <td>112.0</td>\n",
       "      <td>A_PARTLYSUBMITTED_COMPLETE</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.153982</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33572</th>\n",
       "      <td>112.0</td>\n",
       "      <td>W_Afhandelen leads_SCHEDULE</td>\n",
       "      <td>2012-01-13 11:46:52</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>20.056957</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33573</th>\n",
       "      <td>11169.0</td>\n",
       "      <td>W_Afhandelen leads_START</td>\n",
       "      <td>2012-01-13 12:38:46</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.036215</td>\n",
       "      <td>17.387993</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33574</th>\n",
       "      <td>11169.0</td>\n",
       "      <td>W_Afhandelen leads_COMPLETE</td>\n",
       "      <td>2012-01-13 12:42:19</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.038681</td>\n",
       "      <td>14.183450</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33575</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>W_Afhandelen leads_START</td>\n",
       "      <td>2012-01-13 12:44:00</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.039850</td>\n",
       "      <td>12.190316</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33576</th>\n",
       "      <td>10910.0</td>\n",
       "      <td>W_Afhandelen leads_COMPLETE</td>\n",
       "      <td>2012-01-13 14:01:47</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.093866</td>\n",
       "      <td>0.093866</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33577</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Afhandelen leads_START</td>\n",
       "      <td>2012-01-13 14:26:58</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.111354</td>\n",
       "      <td>0.111354</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33578</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Afhandelen leads_COMPLETE</td>\n",
       "      <td>2012-01-13 14:28:21</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.112315</td>\n",
       "      <td>0.112315</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33579</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Afhandelen leads_START</td>\n",
       "      <td>2012-01-13 14:28:24</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.112350</td>\n",
       "      <td>0.112350</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33580</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Afhandelen leads_COMPLETE</td>\n",
       "      <td>2012-01-13 14:30:22</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.113715</td>\n",
       "      <td>0.113715</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33581</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Afhandelen leads_START</td>\n",
       "      <td>2012-01-13 15:06:39</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.138912</td>\n",
       "      <td>0.138912</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33582</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Afhandelen leads_COMPLETE</td>\n",
       "      <td>2012-01-13 15:09:47</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.141088</td>\n",
       "      <td>0.141088</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33583</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Afhandelen leads_START</td>\n",
       "      <td>2012-01-13 15:09:49</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.141111</td>\n",
       "      <td>0.141111</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33584</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>A_DECLINED_COMPLETE</td>\n",
       "      <td>2012-01-13 15:10:56</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.141887</td>\n",
       "      <td>0.141887</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33585</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Afhandelen leads_COMPLETE</td>\n",
       "      <td>2012-01-13 15:11:01</td>\n",
       "      <td>2012-01-13 11:46:37</td>\n",
       "      <td>200524</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.141944</td>\n",
       "      <td>0.141944</td>\n",
       "      <td>0.141944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       org:resource                 concept:name time:timestamp_short  \\\n",
       "33570         112.0         A_SUBMITTED_COMPLETE  2012-01-13 11:46:37   \n",
       "33571         112.0   A_PARTLYSUBMITTED_COMPLETE  2012-01-13 11:46:37   \n",
       "33572         112.0  W_Afhandelen leads_SCHEDULE  2012-01-13 11:46:52   \n",
       "33573       11169.0     W_Afhandelen leads_START  2012-01-13 12:38:46   \n",
       "33574       11169.0  W_Afhandelen leads_COMPLETE  2012-01-13 12:42:19   \n",
       "33575       10910.0     W_Afhandelen leads_START  2012-01-13 12:44:00   \n",
       "33576       10910.0  W_Afhandelen leads_COMPLETE  2012-01-13 14:01:47   \n",
       "33577       11122.0     W_Afhandelen leads_START  2012-01-13 14:26:58   \n",
       "33578       11122.0  W_Afhandelen leads_COMPLETE  2012-01-13 14:28:21   \n",
       "33579       11122.0     W_Afhandelen leads_START  2012-01-13 14:28:24   \n",
       "33580       11122.0  W_Afhandelen leads_COMPLETE  2012-01-13 14:30:22   \n",
       "33581       11122.0     W_Afhandelen leads_START  2012-01-13 15:06:39   \n",
       "33582       11122.0  W_Afhandelen leads_COMPLETE  2012-01-13 15:09:47   \n",
       "33583       11122.0     W_Afhandelen leads_START  2012-01-13 15:09:49   \n",
       "33584       11122.0          A_DECLINED_COMPLETE  2012-01-13 15:10:56   \n",
       "33585       11122.0  W_Afhandelen leads_COMPLETE  2012-01-13 15:11:01   \n",
       "\n",
       "       case:REG_DATE_short  case:concept:name  case:AMOUNT_REQ  time_spent  \\\n",
       "33570  2012-01-13 11:46:37             200524             5000    0.000000   \n",
       "33571  2012-01-13 11:46:37             200524             5000    0.000000   \n",
       "33572  2012-01-13 11:46:37             200524             5000    0.000174   \n",
       "33573  2012-01-13 11:46:37             200524             5000    0.036215   \n",
       "33574  2012-01-13 11:46:37             200524             5000    0.038681   \n",
       "33575  2012-01-13 11:46:37             200524             5000    0.039850   \n",
       "33576  2012-01-13 11:46:37             200524             5000    0.093866   \n",
       "33577  2012-01-13 11:46:37             200524             5000    0.111354   \n",
       "33578  2012-01-13 11:46:37             200524             5000    0.112315   \n",
       "33579  2012-01-13 11:46:37             200524             5000    0.112350   \n",
       "33580  2012-01-13 11:46:37             200524             5000    0.113715   \n",
       "33581  2012-01-13 11:46:37             200524             5000    0.138912   \n",
       "33582  2012-01-13 11:46:37             200524             5000    0.141088   \n",
       "33583  2012-01-13 11:46:37             200524             5000    0.141111   \n",
       "33584  2012-01-13 11:46:37             200524             5000    0.141887   \n",
       "33585  2012-01-13 11:46:37             200524             5000    0.141944   \n",
       "\n",
       "       total_time_pred  total_time_true  \n",
       "33570        17.969542         0.141944  \n",
       "33571        18.153982         0.141944  \n",
       "33572        20.056957         0.141944  \n",
       "33573        17.387993         0.141944  \n",
       "33574        14.183450         0.141944  \n",
       "33575        12.190316         0.141944  \n",
       "33576         0.093866         0.141944  \n",
       "33577         0.111354         0.141944  \n",
       "33578         0.112315         0.141944  \n",
       "33579         0.112350         0.141944  \n",
       "33580         0.113715         0.141944  \n",
       "33581         0.138912         0.141944  \n",
       "33582         0.141088         0.141944  \n",
       "33583         0.141111         0.141944  \n",
       "33584         0.141887         0.141944  \n",
       "33585         0.141944         0.141944  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总时间很小\n",
    "data_frame[data_frame['case:concept:name']==200524]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>org:resource</th>\n",
       "      <th>concept:name</th>\n",
       "      <th>time:timestamp_short</th>\n",
       "      <th>case:REG_DATE_short</th>\n",
       "      <th>case:concept:name</th>\n",
       "      <th>case:AMOUNT_REQ</th>\n",
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       "      <th>total_time_pred</th>\n",
       "      <th>total_time_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>112.0</td>\n",
       "      <td>A_SUBMITTED_COMPLETE</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
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       "      <td>17.152657</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>112.0</td>\n",
       "      <td>A_PARTLYSUBMITTED_COMPLETE</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.107141</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>112.0</td>\n",
       "      <td>A_PREACCEPTED_COMPLETE</td>\n",
       "      <td>2011-12-10 15:39:22</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>16.998072</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>112.0</td>\n",
       "      <td>W_Completeren aanvraag_SCHEDULE</td>\n",
       "      <td>2011-12-10 15:39:22</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>16.506663</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>NaN</td>\n",
       "      <td>W_Completeren aanvraag_START</td>\n",
       "      <td>2011-12-10 16:12:16</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.023241</td>\n",
       "      <td>14.387231</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826</th>\n",
       "      <td>NaN</td>\n",
       "      <td>W_Completeren aanvraag_COMPLETE</td>\n",
       "      <td>2011-12-10 16:13:42</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.024236</td>\n",
       "      <td>15.036895</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>11009.0</td>\n",
       "      <td>W_Completeren aanvraag_START</td>\n",
       "      <td>2011-12-12 12:20:57</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.862604</td>\n",
       "      <td>18.889744</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>11009.0</td>\n",
       "      <td>W_Completeren aanvraag_COMPLETE</td>\n",
       "      <td>2011-12-12 12:21:06</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.862708</td>\n",
       "      <td>18.287757</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>11009.0</td>\n",
       "      <td>W_Completeren aanvraag_START</td>\n",
       "      <td>2011-12-12 12:21:14</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.862801</td>\n",
       "      <td>17.568669</td>\n",
       "      <td>57.733761</td>\n",
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       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>11009.0</td>\n",
       "      <td>W_Completeren aanvraag_COMPLETE</td>\n",
       "      <td>2011-12-12 12:21:20</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.862870</td>\n",
       "      <td>17.730107</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831</th>\n",
       "      <td>NaN</td>\n",
       "      <td>W_Completeren aanvraag_START</td>\n",
       "      <td>2011-12-12 15:24:47</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.990266</td>\n",
       "      <td>18.128599</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>10880.0</td>\n",
       "      <td>A_ACCEPTED_COMPLETE</td>\n",
       "      <td>2011-12-12 15:35:13</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.997512</td>\n",
       "      <td>20.170516</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1833</th>\n",
       "      <td>10880.0</td>\n",
       "      <td>A_FINALIZED_COMPLETE</td>\n",
       "      <td>2011-12-12 15:39:08</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.000231</td>\n",
       "      <td>19.137402</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>10880.0</td>\n",
       "      <td>O_SELECTED_COMPLETE</td>\n",
       "      <td>2011-12-12 15:39:08</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.000231</td>\n",
       "      <td>16.851319</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>10880.0</td>\n",
       "      <td>O_CREATED_COMPLETE</td>\n",
       "      <td>2011-12-12 15:39:10</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.000255</td>\n",
       "      <td>24.613332</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>10880.0</td>\n",
       "      <td>O_SENT_COMPLETE</td>\n",
       "      <td>2011-12-12 15:39:10</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.000255</td>\n",
       "      <td>21.844417</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>NaN</td>\n",
       "      <td>W_Nabellen offertes_SCHEDULE</td>\n",
       "      <td>2011-12-12 15:39:10</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.000255</td>\n",
       "      <td>19.357892</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>NaN</td>\n",
       "      <td>W_Completeren aanvraag_COMPLETE</td>\n",
       "      <td>2011-12-12 15:39:12</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.000278</td>\n",
       "      <td>16.949822</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1839</th>\n",
       "      <td>10982.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2011-12-17 12:49:39</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>6.882535</td>\n",
       "      <td>17.383031</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>10982.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2011-12-17 12:50:47</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>6.883322</td>\n",
       "      <td>18.909735</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1841</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2011-12-19 15:24:53</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>8.990336</td>\n",
       "      <td>18.484523</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1842</th>\n",
       "      <td>11122.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2011-12-19 15:29:15</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>8.993368</td>\n",
       "      <td>21.068683</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>11180.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2011-12-19 16:28:09</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>9.034271</td>\n",
       "      <td>19.080147</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844</th>\n",
       "      <td>11180.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2011-12-19 16:28:33</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>9.034549</td>\n",
       "      <td>18.484492</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>11181.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-01-05 19:30:11</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>26.160683</td>\n",
       "      <td>32.144560</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>11181.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-01-05 19:30:20</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>26.160787</td>\n",
       "      <td>31.310335</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>11181.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-01-05 20:58:29</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>26.222002</td>\n",
       "      <td>30.788091</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>11181.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-01-05 20:58:47</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>26.222211</td>\n",
       "      <td>34.190036</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-01-06 10:44:49</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>26.795845</td>\n",
       "      <td>30.274971</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-01-06 10:51:42</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>26.800625</td>\n",
       "      <td>29.648667</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-01-06 16:04:11</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>27.017627</td>\n",
       "      <td>34.971518</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>O_CANCELLED_COMPLETE</td>\n",
       "      <td>2012-01-06 16:08:45</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>27.020799</td>\n",
       "      <td>27.045215</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>O_SELECTED_COMPLETE</td>\n",
       "      <td>2012-01-06 16:08:45</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>27.020799</td>\n",
       "      <td>50.003165</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>O_CREATED_COMPLETE</td>\n",
       "      <td>2012-01-06 16:08:47</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>27.020822</td>\n",
       "      <td>48.114570</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>O_SENT_COMPLETE</td>\n",
       "      <td>2012-01-06 16:08:47</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>27.020822</td>\n",
       "      <td>40.106487</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>W_Nabellen offertes_SCHEDULE</td>\n",
       "      <td>2012-01-06 16:08:47</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>27.020822</td>\n",
       "      <td>40.018623</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>10935.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-01-06 16:08:50</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>27.020856</td>\n",
       "      <td>40.854363</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1858</th>\n",
       "      <td>10929.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-01-16 16:52:05</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>37.050891</td>\n",
       "      <td>46.179795</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1859</th>\n",
       "      <td>10929.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-01-16 16:53:27</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>37.051840</td>\n",
       "      <td>51.191525</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>11180.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-01-23 09:27:45</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>43.742326</td>\n",
       "      <td>48.145750</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>11180.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-01-23 09:31:01</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>43.744595</td>\n",
       "      <td>53.923043</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>11181.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-01-27 13:10:32</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>47.897037</td>\n",
       "      <td>52.908321</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>11181.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-01-27 13:11:35</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>47.897766</td>\n",
       "      <td>53.993332</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>10881.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-01-30 18:54:11</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>51.135683</td>\n",
       "      <td>61.594238</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>10881.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-01-30 18:55:30</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>51.136597</td>\n",
       "      <td>64.394111</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>10861.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-02-01 10:24:48</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>52.781944</td>\n",
       "      <td>59.350357</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>10861.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-02-01 10:26:37</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>52.783206</td>\n",
       "      <td>55.050234</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>11180.0</td>\n",
       "      <td>W_Nabellen offertes_START</td>\n",
       "      <td>2012-02-02 19:52:11</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>54.175961</td>\n",
       "      <td>63.698470</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>11180.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-02-02 19:53:13</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>54.176678</td>\n",
       "      <td>65.677989</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>11180.0</td>\n",
       "      <td>W_Nabellen offertes_COMPLETE</td>\n",
       "      <td>2012-02-06 09:15:25</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>57.733762</td>\n",
       "      <td>57.733762</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>112.0</td>\n",
       "      <td>A_CANCELLED_COMPLETE</td>\n",
       "      <td>2012-02-06 09:15:25</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>57.733762</td>\n",
       "      <td>57.733762</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>112.0</td>\n",
       "      <td>O_CANCELLED_COMPLETE</td>\n",
       "      <td>2012-02-06 09:15:25</td>\n",
       "      <td>2011-12-10 15:38:48</td>\n",
       "      <td>192608</td>\n",
       "      <td>20000</td>\n",
       "      <td>57.733762</td>\n",
       "      <td>57.733762</td>\n",
       "      <td>57.733761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      org:resource                     concept:name time:timestamp_short  \\\n",
       "1821         112.0             A_SUBMITTED_COMPLETE  2011-12-10 15:38:48   \n",
       "1822         112.0       A_PARTLYSUBMITTED_COMPLETE  2011-12-10 15:38:48   \n",
       "1823         112.0           A_PREACCEPTED_COMPLETE  2011-12-10 15:39:22   \n",
       "1824         112.0  W_Completeren aanvraag_SCHEDULE  2011-12-10 15:39:22   \n",
       "1825           NaN     W_Completeren aanvraag_START  2011-12-10 16:12:16   \n",
       "1826           NaN  W_Completeren aanvraag_COMPLETE  2011-12-10 16:13:42   \n",
       "1827       11009.0     W_Completeren aanvraag_START  2011-12-12 12:20:57   \n",
       "1828       11009.0  W_Completeren aanvraag_COMPLETE  2011-12-12 12:21:06   \n",
       "1829       11009.0     W_Completeren aanvraag_START  2011-12-12 12:21:14   \n",
       "1830       11009.0  W_Completeren aanvraag_COMPLETE  2011-12-12 12:21:20   \n",
       "1831           NaN     W_Completeren aanvraag_START  2011-12-12 15:24:47   \n",
       "1832       10880.0              A_ACCEPTED_COMPLETE  2011-12-12 15:35:13   \n",
       "1833       10880.0             A_FINALIZED_COMPLETE  2011-12-12 15:39:08   \n",
       "1834       10880.0              O_SELECTED_COMPLETE  2011-12-12 15:39:08   \n",
       "1835       10880.0               O_CREATED_COMPLETE  2011-12-12 15:39:10   \n",
       "1836       10880.0                  O_SENT_COMPLETE  2011-12-12 15:39:10   \n",
       "1837           NaN     W_Nabellen offertes_SCHEDULE  2011-12-12 15:39:10   \n",
       "1838           NaN  W_Completeren aanvraag_COMPLETE  2011-12-12 15:39:12   \n",
       "1839       10982.0        W_Nabellen offertes_START  2011-12-17 12:49:39   \n",
       "1840       10982.0     W_Nabellen offertes_COMPLETE  2011-12-17 12:50:47   \n",
       "1841       11122.0        W_Nabellen offertes_START  2011-12-19 15:24:53   \n",
       "1842       11122.0     W_Nabellen offertes_COMPLETE  2011-12-19 15:29:15   \n",
       "1843       11180.0        W_Nabellen offertes_START  2011-12-19 16:28:09   \n",
       "1844       11180.0     W_Nabellen offertes_COMPLETE  2011-12-19 16:28:33   \n",
       "1845       11181.0        W_Nabellen offertes_START  2012-01-05 19:30:11   \n",
       "1846       11181.0     W_Nabellen offertes_COMPLETE  2012-01-05 19:30:20   \n",
       "1847       11181.0        W_Nabellen offertes_START  2012-01-05 20:58:29   \n",
       "1848       11181.0     W_Nabellen offertes_COMPLETE  2012-01-05 20:58:47   \n",
       "1849       10935.0        W_Nabellen offertes_START  2012-01-06 10:44:49   \n",
       "1850       10935.0     W_Nabellen offertes_COMPLETE  2012-01-06 10:51:42   \n",
       "1851       10935.0        W_Nabellen offertes_START  2012-01-06 16:04:11   \n",
       "1852       10935.0             O_CANCELLED_COMPLETE  2012-01-06 16:08:45   \n",
       "1853       10935.0              O_SELECTED_COMPLETE  2012-01-06 16:08:45   \n",
       "1854       10935.0               O_CREATED_COMPLETE  2012-01-06 16:08:47   \n",
       "1855       10935.0                  O_SENT_COMPLETE  2012-01-06 16:08:47   \n",
       "1856       10935.0     W_Nabellen offertes_SCHEDULE  2012-01-06 16:08:47   \n",
       "1857       10935.0     W_Nabellen offertes_COMPLETE  2012-01-06 16:08:50   \n",
       "1858       10929.0        W_Nabellen offertes_START  2012-01-16 16:52:05   \n",
       "1859       10929.0     W_Nabellen offertes_COMPLETE  2012-01-16 16:53:27   \n",
       "1860       11180.0        W_Nabellen offertes_START  2012-01-23 09:27:45   \n",
       "1861       11180.0     W_Nabellen offertes_COMPLETE  2012-01-23 09:31:01   \n",
       "1862       11181.0        W_Nabellen offertes_START  2012-01-27 13:10:32   \n",
       "1863       11181.0     W_Nabellen offertes_COMPLETE  2012-01-27 13:11:35   \n",
       "1864       10881.0        W_Nabellen offertes_START  2012-01-30 18:54:11   \n",
       "1865       10881.0     W_Nabellen offertes_COMPLETE  2012-01-30 18:55:30   \n",
       "1866       10861.0        W_Nabellen offertes_START  2012-02-01 10:24:48   \n",
       "1867       10861.0     W_Nabellen offertes_COMPLETE  2012-02-01 10:26:37   \n",
       "1868       11180.0        W_Nabellen offertes_START  2012-02-02 19:52:11   \n",
       "1869       11180.0     W_Nabellen offertes_COMPLETE  2012-02-02 19:53:13   \n",
       "1870       11180.0     W_Nabellen offertes_COMPLETE  2012-02-06 09:15:25   \n",
       "1871         112.0             A_CANCELLED_COMPLETE  2012-02-06 09:15:25   \n",
       "1872         112.0             O_CANCELLED_COMPLETE  2012-02-06 09:15:25   \n",
       "\n",
       "      case:REG_DATE_short  case:concept:name  case:AMOUNT_REQ  time_spent  \\\n",
       "1821  2011-12-10 15:38:48             192608            20000    0.000000   \n",
       "1822  2011-12-10 15:38:48             192608            20000    0.000000   \n",
       "1823  2011-12-10 15:38:48             192608            20000    0.000394   \n",
       "1824  2011-12-10 15:38:48             192608            20000    0.000394   \n",
       "1825  2011-12-10 15:38:48             192608            20000    0.023241   \n",
       "1826  2011-12-10 15:38:48             192608            20000    0.024236   \n",
       "1827  2011-12-10 15:38:48             192608            20000    1.862604   \n",
       "1828  2011-12-10 15:38:48             192608            20000    1.862708   \n",
       "1829  2011-12-10 15:38:48             192608            20000    1.862801   \n",
       "1830  2011-12-10 15:38:48             192608            20000    1.862870   \n",
       "1831  2011-12-10 15:38:48             192608            20000    1.990266   \n",
       "1832  2011-12-10 15:38:48             192608            20000    1.997512   \n",
       "1833  2011-12-10 15:38:48             192608            20000    2.000231   \n",
       "1834  2011-12-10 15:38:48             192608            20000    2.000231   \n",
       "1835  2011-12-10 15:38:48             192608            20000    2.000255   \n",
       "1836  2011-12-10 15:38:48             192608            20000    2.000255   \n",
       "1837  2011-12-10 15:38:48             192608            20000    2.000255   \n",
       "1838  2011-12-10 15:38:48             192608            20000    2.000278   \n",
       "1839  2011-12-10 15:38:48             192608            20000    6.882535   \n",
       "1840  2011-12-10 15:38:48             192608            20000    6.883322   \n",
       "1841  2011-12-10 15:38:48             192608            20000    8.990336   \n",
       "1842  2011-12-10 15:38:48             192608            20000    8.993368   \n",
       "1843  2011-12-10 15:38:48             192608            20000    9.034271   \n",
       "1844  2011-12-10 15:38:48             192608            20000    9.034549   \n",
       "1845  2011-12-10 15:38:48             192608            20000   26.160683   \n",
       "1846  2011-12-10 15:38:48             192608            20000   26.160787   \n",
       "1847  2011-12-10 15:38:48             192608            20000   26.222002   \n",
       "1848  2011-12-10 15:38:48             192608            20000   26.222211   \n",
       "1849  2011-12-10 15:38:48             192608            20000   26.795845   \n",
       "1850  2011-12-10 15:38:48             192608            20000   26.800625   \n",
       "1851  2011-12-10 15:38:48             192608            20000   27.017627   \n",
       "1852  2011-12-10 15:38:48             192608            20000   27.020799   \n",
       "1853  2011-12-10 15:38:48             192608            20000   27.020799   \n",
       "1854  2011-12-10 15:38:48             192608            20000   27.020822   \n",
       "1855  2011-12-10 15:38:48             192608            20000   27.020822   \n",
       "1856  2011-12-10 15:38:48             192608            20000   27.020822   \n",
       "1857  2011-12-10 15:38:48             192608            20000   27.020856   \n",
       "1858  2011-12-10 15:38:48             192608            20000   37.050891   \n",
       "1859  2011-12-10 15:38:48             192608            20000   37.051840   \n",
       "1860  2011-12-10 15:38:48             192608            20000   43.742326   \n",
       "1861  2011-12-10 15:38:48             192608            20000   43.744595   \n",
       "1862  2011-12-10 15:38:48             192608            20000   47.897037   \n",
       "1863  2011-12-10 15:38:48             192608            20000   47.897766   \n",
       "1864  2011-12-10 15:38:48             192608            20000   51.135683   \n",
       "1865  2011-12-10 15:38:48             192608            20000   51.136597   \n",
       "1866  2011-12-10 15:38:48             192608            20000   52.781944   \n",
       "1867  2011-12-10 15:38:48             192608            20000   52.783206   \n",
       "1868  2011-12-10 15:38:48             192608            20000   54.175961   \n",
       "1869  2011-12-10 15:38:48             192608            20000   54.176678   \n",
       "1870  2011-12-10 15:38:48             192608            20000   57.733762   \n",
       "1871  2011-12-10 15:38:48             192608            20000   57.733762   \n",
       "1872  2011-12-10 15:38:48             192608            20000   57.733762   \n",
       "\n",
       "      total_time_pred  total_time_true  \n",
       "1821        17.152657        57.733761  \n",
       "1822        17.107141        57.733761  \n",
       "1823        16.998072        57.733761  \n",
       "1824        16.506663        57.733761  \n",
       "1825        14.387231        57.733761  \n",
       "1826        15.036895        57.733761  \n",
       "1827        18.889744        57.733761  \n",
       "1828        18.287757        57.733761  \n",
       "1829        17.568669        57.733761  \n",
       "1830        17.730107        57.733761  \n",
       "1831        18.128599        57.733761  \n",
       "1832        20.170516        57.733761  \n",
       "1833        19.137402        57.733761  \n",
       "1834        16.851319        57.733761  \n",
       "1835        24.613332        57.733761  \n",
       "1836        21.844417        57.733761  \n",
       "1837        19.357892        57.733761  \n",
       "1838        16.949822        57.733761  \n",
       "1839        17.383031        57.733761  \n",
       "1840        18.909735        57.733761  \n",
       "1841        18.484523        57.733761  \n",
       "1842        21.068683        57.733761  \n",
       "1843        19.080147        57.733761  \n",
       "1844        18.484492        57.733761  \n",
       "1845        32.144560        57.733761  \n",
       "1846        31.310335        57.733761  \n",
       "1847        30.788091        57.733761  \n",
       "1848        34.190036        57.733761  \n",
       "1849        30.274971        57.733761  \n",
       "1850        29.648667        57.733761  \n",
       "1851        34.971518        57.733761  \n",
       "1852        27.045215        57.733761  \n",
       "1853        50.003165        57.733761  \n",
       "1854        48.114570        57.733761  \n",
       "1855        40.106487        57.733761  \n",
       "1856        40.018623        57.733761  \n",
       "1857        40.854363        57.733761  \n",
       "1858        46.179795        57.733761  \n",
       "1859        51.191525        57.733761  \n",
       "1860        48.145750        57.733761  \n",
       "1861        53.923043        57.733761  \n",
       "1862        52.908321        57.733761  \n",
       "1863        53.993332        57.733761  \n",
       "1864        61.594238        57.733761  \n",
       "1865        64.394111        57.733761  \n",
       "1866        59.350357        57.733761  \n",
       "1867        55.050234        57.733761  \n",
       "1868        63.698470        57.733761  \n",
       "1869        65.677989        57.733761  \n",
       "1870        57.733762        57.733761  \n",
       "1871        57.733762        57.733761  \n",
       "1872        57.733762        57.733761  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总时间很长\n",
    "data_frame[data_frame['case:concept:name']==192608]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 其它\n",
    "- 问题\n",
    "    - 需要预测的点数（比例调节）难以找到关联的特征\n",
    "    - 重复活动较多，把活动类别作为action存在问题：考虑活动出现的次数，action空间较大，且one-hot编码后活动之间的关系难以刻画；不考虑活动出现的次数，只能在活动第一次出现的位置进行预测。效果不好。\n",
    "- TODO\n",
    "    - 可以根据一个event和上一个event的间隔时间确定milestone，固定在该点进行预测\n",
    "    - 同一时刻或相近时刻的点可以减少预测，如可以延迟预测，延迟5/10分钟统一决策是否需要预测\n",
    "    - 统计结束状态、不同活动次数与总时间的相关性"
   ]
  }
 ],
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